Overview

Dataset statistics

Number of variables14
Number of observations244
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.8 KiB
Average record size in memory112.5 B

Variable types

Numeric11
Categorical3

Alerts

year has constant value ""Constant
day is highly overall correlated with DMC and 1 other fieldsHigh correlation
Temperature is highly overall correlated with RH and 6 other fieldsHigh correlation
RH is highly overall correlated with Temperature and 4 other fieldsHigh correlation
Rain is highly overall correlated with FFMC and 5 other fieldsHigh correlation
FFMC is highly overall correlated with Temperature and 8 other fieldsHigh correlation
DMC is highly overall correlated with day and 9 other fieldsHigh correlation
DC is highly overall correlated with Rain and 6 other fieldsHigh correlation
ISI is highly overall correlated with Temperature and 8 other fieldsHigh correlation
BUI is highly overall correlated with day and 8 other fieldsHigh correlation
FWI is highly overall correlated with Temperature and 8 other fieldsHigh correlation
Classes is highly overall correlated with Temperature and 6 other fieldsHigh correlation
Rain has 133 (54.5%) zerosZeros
ISI has 4 (1.6%) zerosZeros
FWI has 9 (3.7%) zerosZeros

Reproduction

Analysis started2023-04-09 11:01:44.664935
Analysis finished2023-04-09 11:02:10.821867
Duration26.16 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

day
Real number (ℝ)

Distinct31
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.754098
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:11.044424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8250592
Coefficient of variation (CV)0.56017546
Kurtosis-1.1986502
Mean15.754098
Median Absolute Deviation (MAD)8
Skewness0.0028064126
Sum3844
Variance77.88167
MonotonicityNot monotonic
2023-04-09T16:32:11.279232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 8
 
3.3%
17 8
 
3.3%
30 8
 
3.3%
29 8
 
3.3%
28 8
 
3.3%
27 8
 
3.3%
26 8
 
3.3%
25 8
 
3.3%
24 8
 
3.3%
23 8
 
3.3%
Other values (21) 164
67.2%
ValueCountFrequency (%)
1 8
3.3%
2 8
3.3%
3 8
3.3%
4 8
3.3%
5 8
3.3%
6 8
3.3%
7 8
3.3%
8 8
3.3%
9 8
3.3%
10 8
3.3%
ValueCountFrequency (%)
31 4
1.6%
30 8
3.3%
29 8
3.3%
28 8
3.3%
27 8
3.3%
26 8
3.3%
25 8
3.3%
24 8
3.3%
23 8
3.3%
22 8
3.3%

month
Categorical

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
7.0
62 
8.0
62 
6.0
60 
9.0
60 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters732
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.0
2nd row6.0
3rd row6.0
4th row6.0
5th row6.0

Common Values

ValueCountFrequency (%)
7.0 62
25.4%
8.0 62
25.4%
6.0 60
24.6%
9.0 60
24.6%

Length

2023-04-09T16:32:11.513628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T16:32:11.711679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
7.0 62
25.4%
8.0 62
25.4%
6.0 60
24.6%
9.0 60
24.6%

Most occurring characters

ValueCountFrequency (%)
. 244
33.3%
0 244
33.3%
7 62
 
8.5%
8 62
 
8.5%
6 60
 
8.2%
9 60
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 488
66.7%
Other Punctuation 244
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 244
50.0%
7 62
 
12.7%
8 62
 
12.7%
6 60
 
12.3%
9 60
 
12.3%
Other Punctuation
ValueCountFrequency (%)
. 244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 244
33.3%
0 244
33.3%
7 62
 
8.5%
8 62
 
8.5%
6 60
 
8.2%
9 60
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 244
33.3%
0 244
33.3%
7 62
 
8.5%
8 62
 
8.5%
6 60
 
8.2%
9 60
 
8.2%

year
Categorical

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2012.0
244 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1464
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012.0
2nd row2012.0
3rd row2012.0
4th row2012.0
5th row2012.0

Common Values

ValueCountFrequency (%)
2012.0 244
100.0%

Length

2023-04-09T16:32:11.867938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T16:32:12.007503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2012.0 244
100.0%

Most occurring characters

ValueCountFrequency (%)
2 488
33.3%
0 488
33.3%
1 244
16.7%
. 244
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1220
83.3%
Other Punctuation 244
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 488
40.0%
0 488
40.0%
1 244
20.0%
Other Punctuation
ValueCountFrequency (%)
. 244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1464
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 488
33.3%
0 488
33.3%
1 244
16.7%
. 244
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 488
33.3%
0 488
33.3%
1 244
16.7%
. 244
16.7%

Temperature
Real number (ℝ)

Distinct19
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.172131
Minimum22
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:12.179365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26
Q130
median32
Q335
95-th percentile37.85
Maximum42
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6338433
Coefficient of variation (CV)0.11295003
Kurtosis-0.15431038
Mean32.172131
Median Absolute Deviation (MAD)3
Skewness-0.19630888
Sum7850
Variance13.204817
MonotonicityNot monotonic
2023-04-09T16:32:12.382497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
35 29
11.9%
31 25
10.2%
34 24
9.8%
33 23
9.4%
30 22
9.0%
32 21
8.6%
36 21
8.6%
29 18
7.4%
28 15
6.1%
37 9
 
3.7%
Other values (9) 37
15.2%
ValueCountFrequency (%)
22 2
 
0.8%
24 3
 
1.2%
25 6
 
2.5%
26 5
 
2.0%
27 8
 
3.3%
28 15
6.1%
29 18
7.4%
30 22
9.0%
31 25
10.2%
32 21
8.6%
ValueCountFrequency (%)
42 1
 
0.4%
40 3
 
1.2%
39 6
 
2.5%
38 3
 
1.2%
37 9
 
3.7%
36 21
8.6%
35 29
11.9%
34 24
9.8%
33 23
9.4%
32 21
8.6%

RH
Real number (ℝ)

Distinct62
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.938525
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:12.684050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37
Q152
median63
Q373.25
95-th percentile86
Maximum90
Range69
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation14.8842
Coefficient of variation (CV)0.24030602
Kurtosis-0.53032787
Mean61.938525
Median Absolute Deviation (MAD)11
Skewness-0.23796439
Sum15113
Variance221.53942
MonotonicityNot monotonic
2023-04-09T16:32:12.915292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 10
 
4.1%
55 10
 
4.1%
58 8
 
3.3%
54 8
 
3.3%
78 8
 
3.3%
68 7
 
2.9%
66 7
 
2.9%
73 7
 
2.9%
80 7
 
2.9%
65 7
 
2.9%
Other values (52) 165
67.6%
ValueCountFrequency (%)
21 1
 
0.4%
24 1
 
0.4%
26 1
 
0.4%
29 1
 
0.4%
31 1
 
0.4%
33 2
0.8%
34 3
1.2%
35 1
 
0.4%
36 1
 
0.4%
37 4
1.6%
ValueCountFrequency (%)
90 1
 
0.4%
89 3
1.2%
88 3
1.2%
87 4
1.6%
86 3
1.2%
84 2
 
0.8%
83 1
 
0.4%
82 3
1.2%
81 6
2.5%
80 7
2.9%

Ws
Real number (ℝ)

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.504098
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:13.152331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q317
95-th percentile20
Maximum29
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8101784
Coefficient of variation (CV)0.1812539
Kurtosis2.6021558
Mean15.504098
Median Absolute Deviation (MAD)2
Skewness0.54588125
Sum3783
Variance7.8971025
MonotonicityNot monotonic
2023-04-09T16:32:13.321723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
14 43
17.6%
15 40
16.4%
13 30
12.3%
17 28
11.5%
16 27
11.1%
18 26
10.7%
19 15
 
6.1%
21 8
 
3.3%
11 7
 
2.9%
12 7
 
2.9%
Other values (8) 13
 
5.3%
ValueCountFrequency (%)
6 1
 
0.4%
8 1
 
0.4%
9 2
 
0.8%
10 3
 
1.2%
11 7
 
2.9%
12 7
 
2.9%
13 30
12.3%
14 43
17.6%
15 40
16.4%
16 27
11.1%
ValueCountFrequency (%)
29 1
 
0.4%
26 1
 
0.4%
22 2
 
0.8%
21 8
 
3.3%
20 2
 
0.8%
19 15
 
6.1%
18 26
10.7%
17 28
11.5%
16 27
11.1%
15 40
16.4%

Rain
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76065574
Minimum0
Maximum16.8
Zeros133
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:13.509594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile4.355
Maximum16.8
Range16.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.9994056
Coefficient of variation (CV)2.6285289
Kurtosis25.942123
Mean0.76065574
Median Absolute Deviation (MAD)0
Skewness4.5790706
Sum185.6
Variance3.9976226
MonotonicityNot monotonic
2023-04-09T16:32:13.712404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 133
54.5%
0.1 18
 
7.4%
0.2 12
 
4.9%
0.3 10
 
4.1%
0.4 8
 
3.3%
0.7 6
 
2.5%
0.6 6
 
2.5%
0.5 5
 
2.0%
1.1 3
 
1.2%
1.2 3
 
1.2%
Other values (29) 40
 
16.4%
ValueCountFrequency (%)
0 133
54.5%
0.1 18
 
7.4%
0.2 12
 
4.9%
0.3 10
 
4.1%
0.4 8
 
3.3%
0.5 5
 
2.0%
0.6 6
 
2.5%
0.7 6
 
2.5%
0.8 2
 
0.8%
0.9 1
 
0.4%
ValueCountFrequency (%)
16.8 1
0.4%
13.1 1
0.4%
10.1 1
0.4%
8.7 1
0.4%
8.3 1
0.4%
7.2 1
0.4%
6.5 1
0.4%
6 1
0.4%
5.8 1
0.4%
4.7 1
0.4%

FFMC
Real number (ℝ)

Distinct173
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.887705
Minimum28.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:13.931194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile47.145
Q172.075
median83.5
Q388.3
95-th percentile92.185
Maximum96
Range67.4
Interquartile range (IQR)16.225

Descriptive statistics

Standard deviation14.337571
Coefficient of variation (CV)0.18408003
Kurtosis1.0552083
Mean77.887705
Median Absolute Deviation (MAD)5.7
Skewness-1.3256333
Sum19004.6
Variance205.56594
MonotonicityNot monotonic
2023-04-09T16:32:14.134857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.9 8
 
3.3%
89.4 5
 
2.0%
89.3 4
 
1.6%
85.4 4
 
1.6%
89.1 4
 
1.6%
78.3 3
 
1.2%
88.1 3
 
1.2%
88.3 3
 
1.2%
47.4 3
 
1.2%
79.9 3
 
1.2%
Other values (163) 204
83.6%
ValueCountFrequency (%)
28.6 1
0.4%
30.5 1
0.4%
36.1 1
0.4%
37.3 1
0.4%
37.9 1
0.4%
40.9 1
0.4%
41.1 1
0.4%
42.6 1
0.4%
44.9 1
0.4%
45 1
0.4%
ValueCountFrequency (%)
96 1
0.4%
94.3 1
0.4%
94.2 1
0.4%
93.9 2
0.8%
93.8 1
0.4%
93.7 1
0.4%
93.3 1
0.4%
93 1
0.4%
92.5 2
0.8%
92.2 2
0.8%

DMC
Real number (ℝ)

Distinct166
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.673361
Minimum0.7
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:14.354246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.9
Q15.8
median11.3
Q320.75
95-th percentile41.01
Maximum65.9
Range65.2
Interquartile range (IQR)14.95

Descriptive statistics

Standard deviation12.368039
Coefficient of variation (CV)0.84289067
Kurtosis2.4875981
Mean14.673361
Median Absolute Deviation (MAD)6.9
Skewness1.5276524
Sum3580.3
Variance152.96838
MonotonicityNot monotonic
2023-04-09T16:32:14.541759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.9 5
 
2.0%
12.5 4
 
1.6%
1.9 4
 
1.6%
3.4 3
 
1.2%
4.6 3
 
1.2%
16 3
 
1.2%
6 3
 
1.2%
3.2 3
 
1.2%
9.7 3
 
1.2%
2.6 3
 
1.2%
Other values (156) 210
86.1%
ValueCountFrequency (%)
0.7 1
 
0.4%
0.9 2
0.8%
1.1 2
0.8%
1.2 1
 
0.4%
1.3 3
1.2%
1.7 1
 
0.4%
1.9 4
1.6%
2.1 1
 
0.4%
2.2 2
0.8%
2.4 1
 
0.4%
ValueCountFrequency (%)
65.9 1
0.4%
61.3 1
0.4%
56.3 1
0.4%
54.2 1
0.4%
51.3 1
0.4%
50.2 1
0.4%
47 1
0.4%
46.6 1
0.4%
46.1 1
0.4%
45.6 1
0.4%

DC
Real number (ℝ)

Distinct198
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.288484
Minimum6.9
Maximum220.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:14.758440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile7.6
Q113.275
median33.1
Q368.15
95-th percentile158.86
Maximum220.4
Range213.5
Interquartile range (IQR)54.875

Descriptive statistics

Standard deviation47.619393
Coefficient of variation (CV)0.96613629
Kurtosis1.6141437
Mean49.288484
Median Absolute Deviation (MAD)23.9
Skewness1.4790558
Sum12026.39
Variance2267.6066
MonotonicityNot monotonic
2023-04-09T16:32:14.961524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 5
 
2.0%
7.6 4
 
1.6%
7.8 4
 
1.6%
8.4 4
 
1.6%
7.5 4
 
1.6%
8.3 4
 
1.6%
8.2 4
 
1.6%
17 3
 
1.2%
16.6 2
 
0.8%
10 2
 
0.8%
Other values (188) 208
85.2%
ValueCountFrequency (%)
6.9 1
 
0.4%
7 2
0.8%
7.1 1
 
0.4%
7.3 2
0.8%
7.4 2
0.8%
7.5 4
1.6%
7.6 4
1.6%
7.7 2
0.8%
7.8 4
1.6%
7.9 1
 
0.4%
ValueCountFrequency (%)
220.4 1
0.4%
210.4 1
0.4%
200.2 1
0.4%
190.6 1
0.4%
181.3 1
0.4%
180.4 1
0.4%
177.3 1
0.4%
171.3 1
0.4%
168.2 1
0.4%
167.2 1
0.4%

ISI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct106
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7741803
Minimum0
Maximum19
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:15.211532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.4
median3.5
Q37.3
95-th percentile13.37
Maximum19
Range19
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation4.1753181
Coefficient of variation (CV)0.87456229
Kurtosis0.78335517
Mean4.7741803
Median Absolute Deviation (MAD)2.4
Skewness1.1219755
Sum1164.9
Variance17.433281
MonotonicityNot monotonic
2023-04-09T16:32:15.490877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 8
 
3.3%
1.2 7
 
2.9%
0.4 5
 
2.0%
4.7 5
 
2.0%
5.2 5
 
2.0%
1.5 5
 
2.0%
2.8 5
 
2.0%
1 5
 
2.0%
5.6 5
 
2.0%
2.2 4
 
1.6%
Other values (96) 190
77.9%
ValueCountFrequency (%)
0 4
1.6%
0.1 4
1.6%
0.2 4
1.6%
0.3 3
1.2%
0.4 5
2.0%
0.5 2
 
0.8%
0.6 4
1.6%
0.7 4
1.6%
0.8 3
1.2%
0.9 2
 
0.8%
ValueCountFrequency (%)
19 1
0.4%
18.5 1
0.4%
17.2 1
0.4%
16.6 1
0.4%
16 1
0.4%
15.7 2
0.8%
15.5 1
0.4%
14.3 1
0.4%
14.2 1
0.4%
13.8 2
0.8%

BUI
Real number (ℝ)

Distinct174
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.664754
Minimum1.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:15.729218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.43
Q16
median12.25
Q322.525
95-th percentile46.35
Maximum68
Range66.9
Interquartile range (IQR)16.525

Descriptive statistics

Standard deviation14.204824
Coefficient of variation (CV)0.85238725
Kurtosis1.9791498
Mean16.664754
Median Absolute Deviation (MAD)7.2
Skewness1.4590686
Sum4066.2
Variance201.77702
MonotonicityNot monotonic
2023-04-09T16:32:15.948002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5
 
2.0%
5.1 4
 
1.6%
8.3 3
 
1.2%
7.7 3
 
1.2%
14.2 3
 
1.2%
22.4 3
 
1.2%
11.5 3
 
1.2%
10.9 3
 
1.2%
3.9 3
 
1.2%
2.9 3
 
1.2%
Other values (164) 211
86.5%
ValueCountFrequency (%)
1.1 1
 
0.4%
1.4 2
0.8%
1.6 2
0.8%
1.7 2
0.8%
1.8 2
0.8%
2.2 1
 
0.4%
2.4 3
1.2%
2.6 2
0.8%
2.7 2
0.8%
2.8 2
0.8%
ValueCountFrequency (%)
68 1
0.4%
67.4 1
0.4%
64 1
0.4%
62.9 1
0.4%
59.5 1
0.4%
59.3 1
0.4%
57.1 1
0.4%
54.9 1
0.4%
54.7 1
0.4%
50.9 1
0.4%

FWI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct126
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0353909
Minimum0
Maximum31.1
Zeros9
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-04-09T16:32:16.166749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median4.45
Q311.375
95-th percentile21.495
Maximum31.1
Range31.1
Interquartile range (IQR)10.675

Descriptive statistics

Standard deviation7.4252421
Coefficient of variation (CV)1.0554129
Kurtosis0.67002141
Mean7.0353909
Median Absolute Deviation (MAD)4.05
Skewness1.149922
Sum1716.6354
Variance55.134221
MonotonicityNot monotonic
2023-04-09T16:32:16.369883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 12
 
4.9%
0.8 10
 
4.1%
0.5 9
 
3.7%
0.1 9
 
3.7%
0 9
 
3.7%
0.3 8
 
3.3%
0.9 7
 
2.9%
0.2 6
 
2.5%
0.7 5
 
2.0%
0.6 4
 
1.6%
Other values (116) 165
67.6%
ValueCountFrequency (%)
0 9
3.7%
0.1 9
3.7%
0.2 6
2.5%
0.3 8
3.3%
0.4 12
4.9%
0.5 9
3.7%
0.6 4
 
1.6%
0.7 5
2.0%
0.8 10
4.1%
0.9 7
2.9%
ValueCountFrequency (%)
31.1 1
0.4%
30.3 1
0.4%
30.2 1
0.4%
30 1
0.4%
26.9 1
0.4%
26.3 1
0.4%
26.1 1
0.4%
25.4 1
0.4%
24.5 1
0.4%
24 1
0.4%

Classes
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
fire
138 
not fire
106 

Length

Max length8
Median length4
Mean length5.7377049
Min length4

Characters and Unicode

Total characters1400
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot fire
2nd rownot fire
3rd rownot fire
4th rownot fire
5th rownot fire

Common Values

ValueCountFrequency (%)
fire 138
56.6%
not fire 106
43.4%

Length

2023-04-09T16:32:16.604209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T16:32:16.853446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
fire 244
69.7%
not 106
30.3%

Most occurring characters

ValueCountFrequency (%)
f 244
17.4%
i 244
17.4%
r 244
17.4%
e 244
17.4%
n 106
7.6%
o 106
7.6%
t 106
7.6%
106
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1294
92.4%
Space Separator 106
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 244
18.9%
i 244
18.9%
r 244
18.9%
e 244
18.9%
n 106
8.2%
o 106
8.2%
t 106
8.2%
Space Separator
ValueCountFrequency (%)
106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1294
92.4%
Common 106
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 244
18.9%
i 244
18.9%
r 244
18.9%
e 244
18.9%
n 106
8.2%
o 106
8.2%
t 106
8.2%
Common
ValueCountFrequency (%)
106
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 244
17.4%
i 244
17.4%
r 244
17.4%
e 244
17.4%
n 106
7.6%
o 106
7.6%
t 106
7.6%
106
7.6%

Interactions

2023-04-09T16:32:08.011250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:45.764472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:47.998671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:50.616862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:52.687854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:54.999127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:57.405624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:59.586727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:01.920791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:03.736539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:05.907169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:08.181396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:45.951068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:48.258665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:50.799335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:52.886998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:55.203958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:57.596265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:59.789629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:02.104350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:03.952046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:06.095065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:08.369506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:46.136996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:48.599172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:50.987781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:53.095367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:55.461823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:57.814837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:00.050110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:02.277730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:04.213861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:06.285361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:08.711946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:46.306566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:48.878405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:51.171183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:53.302472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:55.667699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:58.011213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:00.284569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:02.450087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:04.414707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:06.478523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:08.856435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:46.476083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:49.084881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:51.359951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:53.599725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:55.923319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:58.259570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:00.454128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:02.599135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:04.597199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:06.738941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:09.028568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:46.639963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:49.285628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:51.519789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:53.781917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:56.250062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:58.484711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:00.783281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:02.758761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:04.781708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:06.954878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:09.196380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:46.854681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:49.470816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:51.703204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:54.012017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:56.450226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:58.708198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:00.999114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:02.946688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:04.952036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:07.148047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:09.365842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:47.087445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:49.728697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:51.862153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:54.235494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:56.636294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:58.895608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:01.200042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:03.105213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:05.120959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:07.334950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:09.537812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:47.329304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:49.963224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:52.012383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:54.420984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:56.854977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:59.059656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:01.397276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:03.246145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:05.288860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:07.482385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:09.782094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:47.529534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:50.232421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:52.222365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:54.636603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:57.036483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:59.236676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:01.579454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:03.395825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:05.512491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:07.664482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:10.016558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:47.821772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:50.420668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:52.436116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:54.818673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:57.232156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:31:59.411933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:01.762724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:03.567616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:05.727015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-09T16:32:07.837406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-04-09T16:32:17.260225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
dayTemperatureRHWsRainFFMCDMCDCISIBUIFWImonthClasses
day1.0000.121-0.0880.070-0.1700.2500.5030.4790.2360.5170.3470.0000.228
Temperature0.1211.000-0.647-0.216-0.2880.6670.6100.4960.6510.5810.6550.3890.503
RH-0.088-0.6471.0000.1920.175-0.667-0.504-0.339-0.648-0.463-0.5960.2150.410
Ws0.070-0.2160.1921.0000.014-0.0620.0020.0560.0390.0250.0360.1260.140
Rain-0.170-0.2880.1750.0141.000-0.737-0.558-0.614-0.730-0.576-0.7170.0900.348
FFMC0.2500.667-0.667-0.062-0.7371.0000.8210.7290.9890.8040.9670.2610.912
DMC0.5030.610-0.5040.002-0.5580.8211.0000.8900.8200.9880.9160.3320.693
DC0.4790.496-0.3390.056-0.6140.7290.8901.0000.7370.9430.8460.2800.614
ISI0.2360.651-0.6480.039-0.7300.9890.8200.7371.0000.8050.9720.2420.883
BUI0.5170.581-0.4630.025-0.5760.8040.9880.9430.8051.0000.9110.3270.725
FWI0.3470.655-0.5960.036-0.7170.9670.9160.8460.9720.9111.0000.2740.861
month0.0000.3890.2150.1260.0900.2610.3320.2800.2420.3270.2741.0000.341
Classes0.2280.5030.4100.1400.3480.9120.6930.6140.8830.7250.8610.3411.000

Missing values

2023-04-09T16:32:10.313844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-09T16:32:10.644955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

daymonthyearTemperatureRHWsRainFFMCDMCDCISIBUIFWIClasses
01.06.02012.029.057.018.00.065.73.47.61.33.40.5not fire
12.06.02012.029.061.013.01.364.44.17.61.03.90.4not fire
23.06.02012.026.082.022.013.147.12.57.10.32.70.1not fire
34.06.02012.025.089.013.02.528.61.36.90.01.70.0not fire
45.06.02012.027.077.016.00.064.83.014.21.23.90.5not fire
56.06.02012.031.067.014.00.082.65.822.23.17.02.5fire
67.06.02012.033.054.013.00.088.29.930.56.410.97.2fire
78.06.02012.030.073.015.00.086.612.138.35.613.57.1fire
89.06.02012.025.088.013.00.252.97.938.80.410.50.3not fire
910.06.02012.028.079.012.00.073.29.546.31.312.60.9not fire
daymonthyearTemperatureRHWsRainFFMCDMCDCISIBUIFWIClasses
23421.09.02012.035.034.017.00.092.223.697.313.829.421.6fire
23522.09.02012.033.064.013.00.088.926.1106.37.132.413.7fire
23623.09.02012.035.056.014.00.089.029.4115.67.536.015.2fire
23724.09.02012.026.049.06.02.061.311.928.10.611.90.4not fire
23825.09.02012.028.070.015.00.079.913.836.12.414.13.0not fire
23926.09.02012.030.065.014.00.085.416.044.54.516.96.5fire
24027.09.02012.028.087.015.04.441.16.58.00.16.20.0not fire
24128.09.02012.027.087.029.00.545.93.57.90.43.40.2not fire
24229.09.02012.024.054.018.00.179.74.315.21.75.10.7not fire
24330.09.02012.024.064.015.00.267.33.816.51.24.80.5not fire